Abstract
Luminosity function (LF) of galaxies is an important cosmological parameter to understand the evolution and formation of galaxies. However, usual methods fail to give good values for deep samples. We present 5 major improvements to Maximum Likelihood (ML) method: (1) take into account observational errors in magnitudes and redshifts; (2) allow a selection function which determines the detectability of a given galaxy; (3) normalization of the LF within the ML method; (4) combination of heterogeneous samples made out with different selection criteria; and (5) implementation for objective-prism images.
These new improvements are mandatory for a good determination of the LF, and its reliability has been checked by means of simulations.
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References
Efstathiou G., Ellis R.S., Peterson B.A., 1988, MNRAS, 232, 431
García-Dabó C.E., Gallego J., Bongiovanni A et al., 2001, ApSSS, 277, 585
Lynden-Bell D., 1971, MNRAS, 155, 95
Marzke R.O., Da Costa L.N., 1997, AJ, 113, 1
Sandage A., Tammann G.A., Yahil A., 1979, ApJ, 232, 352
SubbaRao M.U., Connolly A.J., Szalay A.S., Koo D.C., 1996, AJ, 112, 3
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© 2003 Springer Science+Business Media Dordrecht
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García-Dabó, C.E., Gallego, J. (2003). Major Improvements to Estimate Luminosity Functions Using Maximum Likelihood Methods. In: Gallego, J., Zamorano, J., Cardiel, N. (eds) Highlights of Spanish Astrophysics III. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1778-6_17
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DOI: https://doi.org/10.1007/978-94-017-1778-6_17
Publisher Name: Springer, Dordrecht
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